Overview

Dataset statistics

Number of variables21
Number of observations106574
Missing cells667732
Missing cells (%)29.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.7 MiB
Average record size in memory154.1 B

Variable types

Numeric8
Categorical8
DateTime2
Unsupported3

Warnings

composer has a high cardinality: 505 distinct values High cardinality
information has a high cardinality: 1586 distinct values High cardinality
license has a high cardinality: 113 distinct values High cardinality
lyricist has a high cardinality: 66 distinct values High cardinality
publisher has a high cardinality: 135 distinct values High cardinality
title has a high cardinality: 94986 distinct values High cardinality
composer has 102904 (96.6%) missing values Missing
date_recorded has 100415 (94.2%) missing values Missing
genre_top has 56976 (53.5%) missing values Missing
information has 104225 (97.8%) missing values Missing
language_code has 91550 (85.9%) missing values Missing
lyricist has 106263 (99.7%) missing values Missing
publisher has 105311 (98.8%) missing values Missing
comments is highly skewed (γ1 = 38.21727055) Skewed
favorites is highly skewed (γ1 = 33.98804676) Skewed
interest is highly skewed (γ1 = 81.71473762) Skewed
listens is highly skewed (γ1 = 24.49991789) Skewed
track_id has unique values Unique
genres is an unsupported type, check if it needs cleaning or further analysis Unsupported
genres_all is an unsupported type, check if it needs cleaning or further analysis Unsupported
tags is an unsupported type, check if it needs cleaning or further analysis Unsupported
comments has 104423 (98.0%) zeros Zeros
favorites has 41979 (39.4%) zeros Zeros
number has 7894 (7.4%) zeros Zeros

Reproduction

Analysis started2021-02-20 09:12:28.237177
Analysis finished2021-02-20 09:12:44.121188
Duration15.88 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

track_id
Real number (ℝ≥0)

UNIQUE

Distinct106574
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79442.63744
Minimum2
Maximum155320
Zeros0
Zeros (%)0.0%
Memory size832.7 KiB
2021-02-20T10:12:44.202914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10100.65
Q140820.25
median78983.5
Q3119422.75
95-th percentile147481.85
Maximum155320
Range155318
Interquartile range (IQR)78602.5

Descriptive statistics

Standard deviation44704.39838
Coefficient of variation (CV)0.5627255064
Kurtosis-1.233487999
Mean79442.63744
Median Absolute Deviation (MAD)39365
Skewness-0.02179694186
Sum8466519642
Variance1998483235
MonotocityStrictly increasing
2021-02-20T10:12:44.277506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40941
 
< 0.1%
833301
 
< 0.1%
1522881
 
< 0.1%
1543371
 
< 0.1%
1481941
 
< 0.1%
1379611
 
< 0.1%
1318181
 
< 0.1%
1338671
 
< 0.1%
1461571
 
< 0.1%
1420631
 
< 0.1%
Other values (106564)106564
> 99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
51
< 0.1%
101
< 0.1%
201
< 0.1%
ValueCountFrequency (%)
1553201
< 0.1%
1553191
< 0.1%
1553181
< 0.1%
1553171
< 0.1%
1553161
< 0.1%

bit_rate
Real number (ℝ)

Distinct12054
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263274.695
Minimum-1
Maximum448000
Zeros0
Zeros (%)0.0%
Memory size832.7 KiB
2021-02-20T10:12:44.360839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile128000
Q1192000
median299914
Q3320000
95-th percentile320000
Maximum448000
Range448001
Interquartile range (IQR)128000

Descriptive statistics

Standard deviation67623.44358
Coefficient of variation (CV)0.2568550828
Kurtosis-0.1983005278
Mean263274.695
Median Absolute Deviation (MAD)20086
Skewness-0.9044918057
Sum2.805823735 × 1010
Variance4572930122
MonotocityNot monotonic
2021-02-20T10:12:44.430581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32000051918
48.7%
25600017211
 
16.1%
19200012876
 
12.1%
1280006119
 
5.7%
1600003859
 
3.6%
64000648
 
0.6%
224000338
 
0.3%
96000238
 
0.2%
32000112
 
0.1%
-1105
 
0.1%
Other values (12044)13150
 
12.3%
ValueCountFrequency (%)
-1105
0.1%
1921
 
< 0.1%
800013
 
< 0.1%
240003
 
< 0.1%
312811
 
< 0.1%
ValueCountFrequency (%)
4480002
< 0.1%
4160001
< 0.1%
3349141
< 0.1%
3330351
< 0.1%
3300421
< 0.1%

comments
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03162122094
Minimum0
Maximum37
Zeros104423
Zeros (%)98.0%
Memory size832.7 KiB
2021-02-20T10:12:44.491998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3219930288
Coefficient of variation (CV)10.18281456
Kurtosis3016.463852
Mean0.03162122094
Median Absolute Deviation (MAD)0
Skewness38.21727055
Sum3370
Variance0.1036795106
MonotocityNot monotonic
2021-02-20T10:12:44.546363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0104423
98.0%
11508
 
1.4%
2415
 
0.4%
3133
 
0.1%
440
 
< 0.1%
519
 
< 0.1%
610
 
< 0.1%
75
 
< 0.1%
85
 
< 0.1%
94
 
< 0.1%
Other values (8)12
 
< 0.1%
ValueCountFrequency (%)
0104423
98.0%
11508
 
1.4%
2415
 
0.4%
3133
 
0.1%
440
 
< 0.1%
ValueCountFrequency (%)
371
< 0.1%
301
< 0.1%
241
< 0.1%
191
< 0.1%
162
< 0.1%

composer
Categorical

HIGH CARDINALITY
MISSING

Distinct505
Distinct (%)13.8%
Missing102904
Missing (%)96.6%
Memory size832.7 KiB
konstantin trokai
541 
Chad Crouch
465 
J.S. Bach
346 
konstantin trokay
223 
Chris Zabriskie
 
89
Other values (500)
2006 

Length

Max length94
Median length14
Mean length15.13950954
Min length1

Characters and Unicode

Total characters55562
Distinct characters91
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)8.0%

Sample

1st rowKurt Vile
2nd rowArc and Sender
3rd rowArc and Sender
4th rowJames Squeaky
5th rowJames Squeaky
ValueCountFrequency (%)
konstantin trokai541
 
0.5%
Chad Crouch465
 
0.4%
J.S. Bach346
 
0.3%
konstantin trokay223
 
0.2%
Chris Zabriskie89
 
0.1%
Jared C. Balogh85
 
0.1%
Shadow Entity Wizard69
 
0.1%
Eddie Palmer48
 
< 0.1%
Franz Joseph Haydn44
 
< 0.1%
Apache Tomcat44
 
< 0.1%
Other values (495)1716
 
1.6%
(Missing)102904
96.6%
2021-02-20T10:12:44.698938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
konstantin798
 
9.3%
trokai577
 
6.7%
crouch465
 
5.4%
chad465
 
5.4%
bach348
 
4.0%
j.s346
 
4.0%
trokay227
 
2.6%
palmer123
 
1.4%
eddie123
 
1.4%
109
 
1.3%
Other values (949)5012
58.3%

Most occurring characters

ValueCountFrequency (%)
a5456
 
9.8%
4956
 
8.9%
n4214
 
7.6%
o3681
 
6.6%
r3648
 
6.6%
t3555
 
6.4%
i3154
 
5.7%
e2841
 
5.1%
h2237
 
4.0%
s2145
 
3.9%
Other values (81)19675
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter41634
74.9%
Uppercase Letter7196
 
13.0%
Space Separator4956
 
8.9%
Other Punctuation1279
 
2.3%
Decimal Number248
 
0.4%
Dash Punctuation90
 
0.2%
Open Punctuation66
 
0.1%
Close Punctuation66
 
0.1%
Math Symbol27
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a5456
13.1%
n4214
10.1%
o3681
8.8%
r3648
8.8%
t3555
8.5%
i3154
 
7.6%
e2841
 
6.8%
h2237
 
5.4%
s2145
 
5.2%
k2027
 
4.9%
Other values (28)8676
20.8%
ValueCountFrequency (%)
C1448
20.1%
J718
10.0%
B709
 
9.9%
S676
 
9.4%
M405
 
5.6%
A386
 
5.4%
T295
 
4.1%
E285
 
4.0%
P255
 
3.5%
D229
 
3.2%
Other values (18)1790
24.9%
ValueCountFrequency (%)
.966
75.5%
,146
 
11.4%
/81
 
6.3%
&36
 
2.8%
;25
 
2.0%
!9
 
0.7%
:7
 
0.5%
'5
 
0.4%
?2
 
0.2%
"2
 
0.2%
ValueCountFrequency (%)
077
31.0%
256
22.6%
150
20.2%
316
 
6.5%
914
 
5.6%
714
 
5.6%
57
 
2.8%
85
 
2.0%
45
 
2.0%
64
 
1.6%
ValueCountFrequency (%)
4956
100.0%
ValueCountFrequency (%)
+27
100.0%
ValueCountFrequency (%)
-90
100.0%
ValueCountFrequency (%)
(66
100.0%
ValueCountFrequency (%)
)66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin48830
87.9%
Common6732
 
12.1%

Most frequent character per script

ValueCountFrequency (%)
a5456
 
11.2%
n4214
 
8.6%
o3681
 
7.5%
r3648
 
7.5%
t3555
 
7.3%
i3154
 
6.5%
e2841
 
5.8%
h2237
 
4.6%
s2145
 
4.4%
k2027
 
4.2%
Other values (56)15872
32.5%
ValueCountFrequency (%)
4956
73.6%
.966
 
14.3%
,146
 
2.2%
-90
 
1.3%
/81
 
1.2%
077
 
1.1%
(66
 
1.0%
)66
 
1.0%
256
 
0.8%
150
 
0.7%
Other values (15)178
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII55378
99.7%
None184
 
0.3%

Most frequent character per block

ValueCountFrequency (%)
a5456
 
9.9%
4956
 
8.9%
n4214
 
7.6%
o3681
 
6.6%
r3648
 
6.6%
t3555
 
6.4%
i3154
 
5.7%
e2841
 
5.1%
h2237
 
4.0%
s2145
 
3.9%
Other values (66)19491
35.2%
ValueCountFrequency (%)
ı42
22.8%
ş31
16.8%
ü20
10.9%
İ20
10.9%
Ö19
10.3%
ö18
9.8%
ç11
 
6.0%
á7
 
3.8%
é6
 
3.3%
ã5
 
2.7%
Other values (5)5
 
2.7%
Distinct86169
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Memory size832.7 KiB
Minimum2008-11-25 17:49:06
Maximum2017-03-30 15:23:39
2021-02-20T10:12:44.768849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:44.849834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

date_recorded
Date

MISSING

Distinct773
Distinct (%)12.6%
Missing100415
Missing (%)94.2%
Memory size832.7 KiB
Minimum1896-01-01 00:00:00
Maximum2017-03-14 00:00:00
2021-02-20T10:12:44.924199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:44.998870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

duration
Real number (ℝ≥0)

Distinct2246
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.8491002
Minimum0
Maximum18350
Zeros16
Zeros (%)< 0.1%
Memory size832.7 KiB
2021-02-20T10:12:45.081034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q1149
median216
Q3305
95-th percentile634.35
Maximum18350
Range18350
Interquartile range (IQR)156

Descriptive statistics

Standard deviation305.5185533
Coefficient of variation (CV)1.099584462
Kurtosis202.1627761
Mean277.8491002
Median Absolute Deviation (MAD)76
Skewness8.414445548
Sum29611490
Variance93341.58644
MonotocityNot monotonic
2021-02-20T10:12:45.151526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180595
 
0.6%
192520
 
0.5%
240510
 
0.5%
60506
 
0.5%
182472
 
0.4%
184472
 
0.4%
210464
 
0.4%
208446
 
0.4%
186440
 
0.4%
216440
 
0.4%
Other values (2236)101709
95.4%
ValueCountFrequency (%)
016
 
< 0.1%
110
 
< 0.1%
213
 
< 0.1%
333
< 0.1%
446
< 0.1%
ValueCountFrequency (%)
183501
< 0.1%
110301
< 0.1%
110161
< 0.1%
109991
< 0.1%
73721
< 0.1%

favorites
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct243
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.182521065
Minimum0
Maximum1482
Zeros41979
Zeros (%)39.4%
Memory size832.7 KiB
2021-02-20T10:12:45.226268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile12
Maximum1482
Range1482
Interquartile range (IQR)3

Descriptive statistics

Standard deviation13.51382047
Coefficient of variation (CV)4.246262693
Kurtosis2228.349417
Mean3.182521065
Median Absolute Deviation (MAD)1
Skewness33.98804676
Sum339174
Variance182.6233436
MonotocityNot monotonic
2021-02-20T10:12:45.297388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041979
39.4%
123666
22.2%
212488
 
11.7%
37324
 
6.9%
44631
 
4.3%
53138
 
2.9%
62389
 
2.2%
71727
 
1.6%
81317
 
1.2%
9976
 
0.9%
Other values (233)6939
 
6.5%
ValueCountFrequency (%)
041979
39.4%
123666
22.2%
212488
 
11.7%
37324
 
6.9%
44631
 
4.3%
ValueCountFrequency (%)
14821
< 0.1%
9611
< 0.1%
7961
< 0.1%
7651
< 0.1%
6331
< 0.1%

genre_top
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing56976
Missing (%)53.5%
Memory size105.0 KiB
Rock
14182 
Experimental
10608 
Electronic
9372 
Hip-Hop
3552 
Folk
2803 
Other values (11)
9081 

Length

Max length19
Median length9
Mean length7.941509738
Min length3

Characters and Unicode

Total characters393883
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHip-Hop
2nd rowHip-Hop
3rd rowHip-Hop
4th rowPop
5th rowHip-Hop
ValueCountFrequency (%)
Rock14182
 
13.3%
Experimental10608
 
10.0%
Electronic9372
 
8.8%
Hip-Hop3552
 
3.3%
Folk2803
 
2.6%
Pop2332
 
2.2%
Instrumental2079
 
2.0%
International1389
 
1.3%
Classical1230
 
1.2%
Jazz571
 
0.5%
Other values (6)1480
 
1.4%
(Missing)56976
53.5%
2021-02-20T10:12:45.443961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rock14182
28.0%
experimental10608
20.9%
electronic9372
18.5%
hip-hop3552
 
7.0%
folk2803
 
5.5%
pop2332
 
4.6%
instrumental2079
 
4.1%
international1389
 
2.7%
classical1230
 
2.4%
jazz571
 
1.1%
Other values (9)2612
 
5.1%

Most occurring characters

ValueCountFrequency (%)
e35167
 
8.9%
o34976
 
8.9%
c34710
 
8.8%
l29550
 
7.5%
n29145
 
7.4%
i27861
 
7.1%
t27688
 
7.0%
r24196
 
6.1%
p20467
 
5.2%
E20004
 
5.1%
Other values (25)110119
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter333284
84.6%
Uppercase Letter54632
 
13.9%
Dash Punctuation4281
 
1.1%
Space Separator1132
 
0.3%
Other Punctuation554
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e35167
10.6%
o34976
10.5%
c34710
10.4%
l29550
8.9%
n29145
8.7%
i27861
8.4%
t27688
8.3%
r24196
7.3%
p20467
6.1%
a18520
 
5.6%
Other values (9)51004
15.3%
ValueCountFrequency (%)
E20004
36.6%
R14357
26.3%
H7658
 
14.0%
I3468
 
6.3%
F2803
 
5.1%
P2332
 
4.3%
C1424
 
2.6%
S598
 
1.1%
J571
 
1.0%
O554
 
1.0%
Other values (3)863
 
1.6%
ValueCountFrequency (%)
-4281
100.0%
ValueCountFrequency (%)
1132
100.0%
ValueCountFrequency (%)
/554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387916
98.5%
Common5967
 
1.5%

Most frequent character per script

ValueCountFrequency (%)
e35167
 
9.1%
o34976
 
9.0%
c34710
 
8.9%
l29550
 
7.6%
n29145
 
7.5%
i27861
 
7.2%
t27688
 
7.1%
r24196
 
6.2%
p20467
 
5.3%
E20004
 
5.2%
Other values (22)104152
26.8%
ValueCountFrequency (%)
-4281
71.7%
1132
 
19.0%
/554
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII393883
100.0%

Most frequent character per block

ValueCountFrequency (%)
e35167
 
8.9%
o34976
 
8.9%
c34710
 
8.8%
l29550
 
7.5%
n29145
 
7.4%
i27861
 
7.1%
t27688
 
7.0%
r24196
 
6.1%
p20467
 
5.2%
E20004
 
5.1%
Other values (25)110119
28.0%

genres
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size832.7 KiB

genres_all
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size832.7 KiB

information
Categorical

HIGH CARDINALITY
MISSING

Distinct1586
Distinct (%)67.5%
Missing104225
Missing (%)97.8%
Memory size832.7 KiB
<p><a href="http://www.myspace.com/theshamblers">The Shamblers</a> shambled down from Bay Ridge, Brooklyn to grace us with a live performance last Wednesday.&nbsp; Led by husband/wife team of Peter and Jess Speer, and ably augmented by drummer Ben Truesdale, the Shamblers brought their blend of Beat Happening/Home Blitz pop roughness plus classic garage punk action with lyrics to match.&nbsp; More info at <a href="http://www.colonialrecordingsusa.com/">their label site</a> (where you can purchase their 100-song mp3 album) and <a href="http://theshamblers.blogspot.com/">their blog</a>.&nbsp; The full set, plus two FCC-unfriendly bonus tracks found below.&nbsp; Engineered by Trent Wolbe.&nbsp; You've been shambled.</p> <p><a href="http://blog.wfmu.org/freeform/2008/07/the-shamblers-l.html" target="_blank">http://blog.wfmu.org/freeform/2008/07/the-shamblers-l.html</a></p> <p><a href="http://www.wfmu.org/playlists/shows/27951" target="_blank">http://www.wfmu.org/playlists/shows/27951</a></p>
 
22
Produced in May 2016.
 
20
<div>We made our music available under the Creative Common license BY, but would like to add the non existing code ‘NAD'</div><p>NAD stands for "No Audio Derivates’ In other words you can freely use (part of) our tracks in a film or video, but cannot remix or re-use part of our tracks in other musical work without our permission. We’d appreciate it if you could let us know where/when you used our music. <br><br>As an extra service we can make custom edits or mixes for you. You can choose fragments, loops, solo-ed instruments, remixes, etc.<br>Please feel free to contact us if you want to know more about using and/or customising our music  in your project.<span class="im"><br></span></p>
 
18
<p><em>by Brian Turner via <a href="http://blog.wfmu.org/freeform/2008/05/negative-approa.html" target="_blank">WFMU's Beware of the Blog!</a></em></p> <p>Back in the late 80's I walked into the Ritz in the middle of the Laughing Hyenas and watched in disbelief while singer John Brannon churned out one hell of a gutteral yowl for the duration of a whole set in almost inhuman fashion. The Hyenas channelled extreme rage through total Stooge/primal Alice Cooper mode with a somewhat bluesy Birthday Party swagger, and eventually I learned he fronted Detroit's hardcore godfathers <a href="http://www.myspace.com/negativeapproach">Negative Approach</a> in the early part of that decade. Later it all came into focus: NA were very much first-wave and represented Detroit as one of the pockets dotted around the USA (see Black Flag, Minor Threat, Bad Brains), with Brannon &amp; company consolidating the midwest brotherhood with the Necros and Meatmen and eventually moving into early singles via Corey Rusk's Touch and Go label (originally a zine, where yes, people learned of the underground way before the internet). As far as first-wave HC goes, Negative Approach were among the most vicious feedback-pummel units going. Songs careened by in seconds, were pretty much politics-free, focusing on sheer vocal/instrumental destruction. They were inspired by not only their American peers, but UK groundbreakers like Discharge (who, like, NA, were big Stooges fans). In 2006 they returned to the stage at the Touch and Go label anniversary, with Brannon and original drummer Chris Moore augmented by Ron Sakowski on bass and Harold Richardson on guitar, reportedly wiped the place down. On the day of their recent Southpaw show, they amazingly agreed in a day's notice to come down to <a href="http://www.wfmu.org/playlists/BT">my show on FMU</a>, plugged in and turned us to mush with 16 songs in 25 minutes.</p> <p>Negative Approach live on WFMU 5/14/08 (broadcast 5/27). Host: Brian Turner, engineer: Richard Rusinkovich (edited-for-FCC version appeared over air). Co-interviewer: Tony Rettman. Ken Freedman shot video of "Hypocrite" and "Dead Stop".</p> <p>Set list: Lost Cause / Can't Tell No One / Sick of Talk / Hypocrite / Dead Stop / Whatever I Do / Tied Down / Evacuate / Your Mistake / Why Be Something That You're Not / Pressure / Fair Warning / Nothing / Borstal Breakout (Sham 69) / Never Surrender (Blitz) /Solitary Confinement.</p> <p>http://www.wfmu.org/playlists/shows/27384</p> <p>Engineered by Richard Rusincovitch<br /></p>
 
18
<p><span style="margin: 0pt 5px; float: left;">Engineered by Jason Engle, released on&nbsp; double cdr&nbsp; by Dan Deacon in 2004. http://creativecommons.org/licenses/by-nc-sa/3.0</span></p>
 
17
Other values (1581)
2254 

Length

Max length13937
Median length238
Mean length741.4108131
Min length2

Characters and Unicode

Total characters1741574
Distinct characters205
Distinct categories21 ?
Distinct scripts5 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1420 ?
Unique (%)60.5%

Sample

1st row<p>Recorded live in downtown Los Angeles at the Lace Gallery, April 27, 1978.</p>
2nd row<p>Recorded live in downtown Los Angeles at the Lace Gallery, April 27, 1978.</p>
3rd row<p>Unreleased</p>
4th row<p>Unreleased</p>
5th row<p><span class="line2">performed by: Ariel Pink (electric guitar, bass, keyboard, drums(sic), voc) recorded on a yamaha MT8X multitrack recorder at the kingsley house, january, 2002. </span></p>
ValueCountFrequency (%)
<p><a href="http://www.myspace.com/theshamblers">The Shamblers</a> shambled down from Bay Ridge, Brooklyn to grace us with a live performance last Wednesday.&nbsp; Led by husband/wife team of Peter and Jess Speer, and ably augmented by drummer Ben Truesdale, the Shamblers brought their blend of Beat Happening/Home Blitz pop roughness plus classic garage punk action with lyrics to match.&nbsp; More info at <a href="http://www.colonialrecordingsusa.com/">their label site</a> (where you can purchase their 100-song mp3 album) and <a href="http://theshamblers.blogspot.com/">their blog</a>.&nbsp; The full set, plus two FCC-unfriendly bonus tracks found below.&nbsp; Engineered by Trent Wolbe.&nbsp; You've been shambled.</p> <p><a href="http://blog.wfmu.org/freeform/2008/07/the-shamblers-l.html" target="_blank">http://blog.wfmu.org/freeform/2008/07/the-shamblers-l.html</a></p> <p><a href="http://www.wfmu.org/playlists/shows/27951" target="_blank">http://www.wfmu.org/playlists/shows/27951</a></p>22
 
< 0.1%
Produced in May 2016.20
 
< 0.1%
<div>We made our music available under the Creative Common license BY, but would like to add the non existing code ‘NAD'</div><p>NAD stands for "No Audio Derivates’ In other words you can freely use (part of) our tracks in a film or video, but cannot remix or re-use part of our tracks in other musical work without our permission. We’d appreciate it if you could let us know where/when you used our music. <br><br>As an extra service we can make custom edits or mixes for you. You can choose fragments, loops, solo-ed instruments, remixes, etc.<br>Please feel free to contact us if you want to know more about using and/or customising our music  in your project.<span class="im"><br></span></p>18
 
< 0.1%
<p><em>by Brian Turner via <a href="http://blog.wfmu.org/freeform/2008/05/negative-approa.html" target="_blank">WFMU's Beware of the Blog!</a></em></p> <p>Back in the late 80's I walked into the Ritz in the middle of the Laughing Hyenas and watched in disbelief while singer John Brannon churned out one hell of a gutteral yowl for the duration of a whole set in almost inhuman fashion. The Hyenas channelled extreme rage through total Stooge/primal Alice Cooper mode with a somewhat bluesy Birthday Party swagger, and eventually I learned he fronted Detroit's hardcore godfathers <a href="http://www.myspace.com/negativeapproach">Negative Approach</a> in the early part of that decade. Later it all came into focus: NA were very much first-wave and represented Detroit as one of the pockets dotted around the USA (see Black Flag, Minor Threat, Bad Brains), with Brannon &amp; company consolidating the midwest brotherhood with the Necros and Meatmen and eventually moving into early singles via Corey Rusk's Touch and Go label (originally a zine, where yes, people learned of the underground way before the internet). As far as first-wave HC goes, Negative Approach were among the most vicious feedback-pummel units going. Songs careened by in seconds, were pretty much politics-free, focusing on sheer vocal/instrumental destruction. They were inspired by not only their American peers, but UK groundbreakers like Discharge (who, like, NA, were big Stooges fans). In 2006 they returned to the stage at the Touch and Go label anniversary, with Brannon and original drummer Chris Moore augmented by Ron Sakowski on bass and Harold Richardson on guitar, reportedly wiped the place down. On the day of their recent Southpaw show, they amazingly agreed in a day's notice to come down to <a href="http://www.wfmu.org/playlists/BT">my show on FMU</a>, plugged in and turned us to mush with 16 songs in 25 minutes.</p> <p>Negative Approach live on WFMU 5/14/08 (broadcast 5/27). Host: Brian Turner, engineer: Richard Rusinkovich (edited-for-FCC version appeared over air). Co-interviewer: Tony Rettman. Ken Freedman shot video of "Hypocrite" and "Dead Stop".</p> <p>Set list: Lost Cause / Can't Tell No One / Sick of Talk / Hypocrite / Dead Stop / Whatever I Do / Tied Down / Evacuate / Your Mistake / Why Be Something That You're Not / Pressure / Fair Warning / Nothing / Borstal Breakout (Sham 69) / Never Surrender (Blitz) /Solitary Confinement.</p> <p>http://www.wfmu.org/playlists/shows/27384</p> <p>Engineered by Richard Rusincovitch<br /></p>18
 
< 0.1%
<p><span style="margin: 0pt 5px; float: left;">Engineered by Jason Engle, released on&nbsp; double cdr&nbsp; by Dan Deacon in 2004. http://creativecommons.org/licenses/by-nc-sa/3.0</span></p>17
 
< 0.1%
<p>Eng. Unknown</p>14
 
< 0.1%
<p><span style="color: #2c2c2c; font-family: 'Droid Serif', Georgia, 'Times New Roman', Times, serif; font-size: 12px; line-height: 18px;">subject to constant change at any given moment.</span></p>14
 
< 0.1%
<p>Bloomfield, New Jersey's very own SUX stops by the WFMU studios for a live set of some blistering hardcore on Pat Duncan's Show, April 25, 2008.</p> <p>Engineered by James Theesfeld.</p> <p><a href="http://www.wfmu.org/playlists/shows/27030" target="_blank">http://www.wfmu.org/playlists/shows/27030</a></p>14
 
< 0.1%
<p><span style="font-family: Verdana,Geneva,Arial; font-size: xx-small;">http://wfmu.org/playlists/shows/29505 <br /></span></p>13
 
< 0.1%
<p>Blend a bit of post-apocalyptic French cabaret and Americana heard at a possessed carnival and you get The Snow, a folk-pop band with elements of classical, punk, and rock. The overall mood of The Snow's tunes is of a sweet serenade taking occasional turns into a hall of mirrors, and they have been likened to Fleetwood Mac sitting around the pool trading songs with M. Ward, Cat Power, and Iron and Wine.</p> <p>Engineered by Irene Trudel.</p> <p><a href="http://www.wfmu.org/playlists/shows/27228" target="_blank">http://www.wfmu.org/playlists/shows/27228</a></p>13
 
< 0.1%
Other values (1576)2186
 
2.1%
(Missing)104225
97.8%
2021-02-20T10:12:45.608121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the10470
 
4.4%
a6786
 
2.8%
and6299
 
2.6%
of5810
 
2.4%
to4956
 
2.1%
in4316
 
1.8%
2621
 
1.1%
is2419
 
1.0%
on2060
 
0.9%
that2035
 
0.9%
Other values (24212)191502
80.0%

Most occurring characters

ValueCountFrequency (%)
231145
 
13.3%
e140531
 
8.1%
a107508
 
6.2%
t102874
 
5.9%
o92013
 
5.3%
n91952
 
5.3%
s91036
 
5.2%
i86497
 
5.0%
r82358
 
4.7%
l55705
 
3.2%
Other values (195)659955
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1253304
72.0%
Space Separator232294
 
13.3%
Other Punctuation88307
 
5.1%
Uppercase Letter61837
 
3.6%
Math Symbol52351
 
3.0%
Decimal Number22444
 
1.3%
Dash Punctuation12919
 
0.7%
Control9240
 
0.5%
Final Punctuation2630
 
0.2%
Close Punctuation2056
 
0.1%
Other values (11)4192
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e140531
11.2%
a107508
 
8.6%
t102874
 
8.2%
o92013
 
7.3%
n91952
 
7.3%
s91036
 
7.3%
i86497
 
6.9%
r82358
 
6.6%
l55705
 
4.4%
h53660
 
4.3%
Other values (60)349170
27.9%
ValueCountFrequency (%)
T5226
 
8.5%
S4896
 
7.9%
I4718
 
7.6%
A4526
 
7.3%
M4343
 
7.0%
C3521
 
5.7%
B3338
 
5.4%
N2880
 
4.7%
D2822
 
4.6%
F2688
 
4.3%
Other values (24)22879
37.0%
ValueCountFrequency (%)
3
 
8.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (19)19
55.9%
ValueCountFrequency (%)
/25623
29.0%
.16041
18.2%
,15222
17.2%
"12589
14.3%
:7835
 
8.9%
;5209
 
5.9%
'2057
 
2.3%
&1831
 
2.1%
?603
 
0.7%
!539
 
0.6%
Other values (11)758
 
0.9%
ValueCountFrequency (%)
04894
21.8%
13682
16.4%
23102
13.8%
92024
9.0%
31890
 
8.4%
81772
 
7.9%
51441
 
6.4%
71306
 
5.8%
41248
 
5.6%
61085
 
4.8%
ValueCountFrequency (%)
<23112
44.1%
>23112
44.1%
=5959
 
11.4%
+117
 
0.2%
|31
 
0.1%
~20
 
< 0.1%
ValueCountFrequency (%)
°3
25.0%
©3
25.0%
3
25.0%
2
16.7%
1
 
8.3%
ValueCountFrequency (%)
231145
99.5%
 1144
 
0.5%
 5
 
< 0.1%
ValueCountFrequency (%)
9196
99.5%
43
 
0.5%
Ÿ1
 
< 0.1%
ValueCountFrequency (%)
-12500
96.8%
393
 
3.0%
26
 
0.2%
ValueCountFrequency (%)
1772
67.4%
856
32.5%
»2
 
0.1%
ValueCountFrequency (%)
840
88.8%
104
 
11.0%
«2
 
0.2%
ValueCountFrequency (%)
´4
57.1%
`2
28.6%
^1
 
14.3%
ValueCountFrequency (%)
(1938
96.2%
[77
 
3.8%
ValueCountFrequency (%)
)1979
96.3%
]77
 
3.7%
ValueCountFrequency (%)
$7
87.5%
£1
 
12.5%
ValueCountFrequency (%)
ˈ4
50.0%
ˌ4
50.0%
ValueCountFrequency (%)
_1151
100.0%
ValueCountFrequency (%)
7
100.0%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1315141
75.5%
Common426398
 
24.5%
Hiragana24
 
< 0.1%
Han10
 
< 0.1%
Inherited1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e140531
 
10.7%
a107508
 
8.2%
t102874
 
7.8%
o92013
 
7.0%
n91952
 
7.0%
s91036
 
6.9%
i86497
 
6.6%
r82358
 
6.3%
l55705
 
4.2%
h53660
 
4.1%
Other values (94)411007
31.3%
ValueCountFrequency (%)
231145
54.2%
/25623
 
6.0%
<23112
 
5.4%
>23112
 
5.4%
.16041
 
3.8%
,15222
 
3.6%
"12589
 
3.0%
-12500
 
2.9%
9196
 
2.2%
:7835
 
1.8%
Other values (61)50023
 
11.7%
ValueCountFrequency (%)
3
 
12.5%
2
 
8.3%
2
 
8.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (9)9
37.5%
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1735843
99.7%
Punctuation4155
 
0.2%
None1513
 
0.1%
Hiragana24
 
< 0.1%
Latin Ext Additional14
 
< 0.1%
CJK10
 
< 0.1%
Modifier Letters8
 
< 0.1%
Geometric Shapes3
 
< 0.1%
Misc Symbols2
 
< 0.1%
Dingbats1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
231145
 
13.3%
e140531
 
8.1%
a107508
 
6.2%
t102874
 
5.9%
o92013
 
5.3%
n91952
 
5.3%
s91036
 
5.2%
i86497
 
5.0%
r82358
 
4.7%
l55705
 
3.2%
Other values (85)654224
37.7%
ValueCountFrequency (%)
 1144
75.6%
é80
 
5.3%
ï20
 
1.3%
í20
 
1.3%
á17
 
1.1%
ó16
 
1.1%
ö16
 
1.1%
ä15
 
1.0%
à12
 
0.8%
ü11
 
0.7%
Other values (49)162
 
10.7%
ValueCountFrequency (%)
1772
42.6%
856
20.6%
840
20.2%
393
 
9.5%
140
 
3.4%
104
 
2.5%
26
 
0.6%
8
 
0.2%
7
 
0.2%
3
 
0.1%
Other values (2)6
 
0.1%
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
ValueCountFrequency (%)
11
78.6%
1
 
7.1%
1
 
7.1%
1
 
7.1%
ValueCountFrequency (%)
3
 
12.5%
2
 
8.3%
2
 
8.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (9)9
37.5%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
ˈ4
50.0%
ˌ4
50.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%

interest
Real number (ℝ≥0)

SKEWED

Distinct14396
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3541.310207
Minimum2
Maximum3293557
Zeros0
Zeros (%)0.0%
Memory size832.7 KiB
2021-02-20T10:12:45.693572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile193
Q1599
median1314
Q33059
95-th percentile11258.35
Maximum3293557
Range3293555
Interquartile range (IQR)2460

Descriptive statistics

Standard deviation19017.43089
Coefficient of variation (CV)5.370168039
Kurtosis10854.36407
Mean3541.310207
Median Absolute Deviation (MAD)896
Skewness81.71473762
Sum377411594
Variance361662677.7
MonotocityNot monotonic
2021-02-20T10:12:45.765774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32074
 
0.1%
30271
 
0.1%
32671
 
0.1%
50371
 
0.1%
34271
 
0.1%
32471
 
0.1%
46971
 
0.1%
58470
 
0.1%
36570
 
0.1%
52469
 
0.1%
Other values (14386)105865
99.3%
ValueCountFrequency (%)
21
 
< 0.1%
34
 
< 0.1%
411
< 0.1%
58
< 0.1%
69
< 0.1%
ValueCountFrequency (%)
32935571
< 0.1%
19913441
< 0.1%
15635551
< 0.1%
13715261
< 0.1%
13141561
< 0.1%

language_code
Categorical

MISSING

Distinct44
Distinct (%)0.3%
Missing91550
Missing (%)85.9%
Memory size832.7 KiB
en
14255 
es
 
204
fr
 
191
pt
 
81
de
 
68
Other values (39)
 
225

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters30048
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en14255
 
13.4%
es204
 
0.2%
fr191
 
0.2%
pt81
 
0.1%
de68
 
0.1%
ru30
 
< 0.1%
it28
 
< 0.1%
tr25
 
< 0.1%
sr23
 
< 0.1%
he12
 
< 0.1%
Other values (34)107
 
0.1%
(Missing)91550
85.9%
2021-02-20T10:12:45.915813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en14255
94.9%
es204
 
1.4%
fr191
 
1.3%
pt81
 
0.5%
de68
 
0.5%
ru30
 
0.2%
it28
 
0.2%
tr25
 
0.2%
sr23
 
0.2%
pl12
 
0.1%
Other values (34)107
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e14561
48.5%
n14261
47.5%
r281
 
0.9%
s241
 
0.8%
f197
 
0.7%
t139
 
0.5%
p93
 
0.3%
d71
 
0.2%
i42
 
0.1%
u34
 
0.1%
Other values (14)128
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter30048
100.0%

Most frequent character per category

ValueCountFrequency (%)
e14561
48.5%
n14261
47.5%
r281
 
0.9%
s241
 
0.8%
f197
 
0.7%
t139
 
0.5%
p93
 
0.3%
d71
 
0.2%
i42
 
0.1%
u34
 
0.1%
Other values (14)128
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin30048
100.0%

Most frequent character per script

ValueCountFrequency (%)
e14561
48.5%
n14261
47.5%
r281
 
0.9%
s241
 
0.8%
f197
 
0.7%
t139
 
0.5%
p93
 
0.3%
d71
 
0.2%
i42
 
0.1%
u34
 
0.1%
Other values (14)128
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30048
100.0%

Most frequent character per block

ValueCountFrequency (%)
e14561
48.5%
n14261
47.5%
r281
 
0.9%
s241
 
0.8%
f197
 
0.7%
t139
 
0.5%
p93
 
0.3%
d71
 
0.2%
i42
 
0.1%
u34
 
0.1%
Other values (14)128
 
0.4%

license
Categorical

HIGH CARDINALITY

Distinct113
Distinct (%)0.1%
Missing87
Missing (%)0.1%
Memory size110.1 KiB
Attribution-Noncommercial-Share Alike 3.0 United States
19250 
Attribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International
17732 
Attribution-NonCommercial-ShareAlike 3.0 International
15260 
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
12133 
Attribution-Noncommercial-No Derivative Works 3.0 United States
10584 
Other values (108)
31528 

Length

Max length79
Median length55
Mean length53.17806868
Min length9

Characters and Unicode

Total characters5662773
Distinct characters59
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowAttribution-NonCommercial-ShareAlike 3.0 International
2nd rowAttribution-NonCommercial-ShareAlike 3.0 International
3rd rowAttribution-NonCommercial-ShareAlike 3.0 International
4th rowAttribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International
5th rowAttribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International
ValueCountFrequency (%)
Attribution-Noncommercial-Share Alike 3.0 United States19250
18.1%
Attribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International17732
16.6%
Attribution-NonCommercial-ShareAlike 3.0 International15260
14.3%
Creative Commons Attribution-NonCommercial-NoDerivatives 4.012133
11.4%
Attribution-Noncommercial-No Derivative Works 3.0 United States10584
9.9%
Attribution-NonCommercial-ShareAlike6907
 
6.5%
Attribution4991
 
4.7%
Attribution-NonCommercial 3.0 International3570
 
3.3%
Attribution-NonCommercial3409
 
3.2%
Attribution-ShareAlike1549
 
1.5%
Other values (103)11102
10.4%
2021-02-20T10:12:46.075267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.071301
16.5%
international38609
 
9.0%
states31652
 
7.3%
united31652
 
7.3%
attribution-noncommercial-noderivatives30679
 
7.1%
attribution-noncommercial-sharealike22495
 
5.2%
alike21299
 
4.9%
attribution-noncommercial-share21017
 
4.9%
music18162
 
4.2%
sharing17750
 
4.1%
Other values (86)126451
29.3%

Most occurring characters

ValueCountFrequency (%)
i558932
 
9.9%
t543600
 
9.6%
o415468
 
7.3%
e405561
 
7.2%
n384755
 
6.8%
r372763
 
6.6%
a365030
 
6.4%
325468
 
5.7%
m216380
 
3.8%
l183252
 
3.2%
Other values (49)1891564
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4226909
74.6%
Uppercase Letter625987
 
11.1%
Space Separator325468
 
5.7%
Dash Punctuation182830
 
3.2%
Decimal Number176474
 
3.1%
Other Punctuation89093
 
1.6%
Open Punctuation18006
 
0.3%
Close Punctuation18006
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i558932
13.2%
t543600
12.9%
o415468
9.8%
e405561
9.6%
n384755
9.1%
r372763
8.8%
a365030
8.6%
m216380
 
5.1%
l183252
 
4.3%
c148408
 
3.5%
Other values (13)632760
15.0%
ValueCountFrequency (%)
A151087
24.1%
N137204
21.9%
S96688
15.4%
C88588
14.2%
D43638
 
7.0%
I38955
 
6.2%
U33584
 
5.4%
M18407
 
2.9%
W11456
 
1.8%
P1445
 
0.2%
Other values (12)4935
 
0.8%
ValueCountFrequency (%)
087890
49.8%
371301
40.4%
412505
 
7.1%
22591
 
1.5%
11410
 
0.8%
5777
 
0.4%
ValueCountFrequency (%)
.87740
98.5%
:918
 
1.0%
/428
 
0.5%
&7
 
< 0.1%
ValueCountFrequency (%)
-182830
100.0%
ValueCountFrequency (%)
325468
100.0%
ValueCountFrequency (%)
(18006
100.0%
ValueCountFrequency (%)
)18006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4852896
85.7%
Common809877
 
14.3%

Most frequent character per script

ValueCountFrequency (%)
i558932
11.5%
t543600
11.2%
o415468
 
8.6%
e405561
 
8.4%
n384755
 
7.9%
r372763
 
7.7%
a365030
 
7.5%
m216380
 
4.5%
l183252
 
3.8%
A151087
 
3.1%
Other values (35)1256068
25.9%
ValueCountFrequency (%)
325468
40.2%
-182830
22.6%
087890
 
10.9%
.87740
 
10.8%
371301
 
8.8%
(18006
 
2.2%
)18006
 
2.2%
412505
 
1.5%
22591
 
0.3%
11410
 
0.2%
Other values (4)2130
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5662773
100.0%

Most frequent character per block

ValueCountFrequency (%)
i558932
 
9.9%
t543600
 
9.6%
o415468
 
7.3%
e405561
 
7.2%
n384755
 
6.8%
r372763
 
6.6%
a365030
 
6.4%
325468
 
5.7%
m216380
 
3.8%
l183252
 
3.2%
Other values (49)1891564
33.4%

listens
Real number (ℝ≥0)

SKEWED

Distinct12035
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2329.353548
Minimum0
Maximum543252
Zeros1
Zeros (%)< 0.1%
Memory size832.7 KiB
2021-02-20T10:12:46.153915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q1292
median764
Q32018
95-th percentile8254.4
Maximum543252
Range543252
Interquartile range (IQR)1726

Descriptive statistics

Standard deviation8028.070647
Coefficient of variation (CV)3.446480099
Kurtosis1021.970761
Mean2329.353548
Median Absolute Deviation (MAD)583
Skewness24.49991789
Sum248248525
Variance64449918.31
MonotocityNot monotonic
2021-02-20T10:12:46.229527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96140
 
0.1%
97136
 
0.1%
141133
 
0.1%
112130
 
0.1%
152129
 
0.1%
114126
 
0.1%
128126
 
0.1%
106126
 
0.1%
122125
 
0.1%
123125
 
0.1%
Other values (12025)105278
98.8%
ValueCountFrequency (%)
01
 
< 0.1%
13
 
< 0.1%
210
< 0.1%
316
< 0.1%
418
< 0.1%
ValueCountFrequency (%)
5432521
< 0.1%
4912351
< 0.1%
4681631
< 0.1%
4339921
< 0.1%
4291681
< 0.1%

lyricist
Categorical

HIGH CARDINALITY
MISSING

Distinct66
Distinct (%)21.2%
Missing106263
Missing (%)99.7%
Memory size832.7 KiB
Apache Tomcat
44 
Wayne Myers
24 
Shadow Entity Wizard
 
19
Wesley Willis
 
18
Kathleen Martin
 
14
Other values (61)
192 

Length

Max length32
Median length13
Mean length12.89389068
Min length5

Characters and Unicode

Total characters4010
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)10.0%

Sample

1st rowDavid Lineal
2nd rowDavid Lineal
3rd rowDavid Lineal
4th rowJad Fair
5th rowJad Fair
ValueCountFrequency (%)
Apache Tomcat44
 
< 0.1%
Wayne Myers24
 
< 0.1%
Shadow Entity Wizard19
 
< 0.1%
Wesley Willis18
 
< 0.1%
Kathleen Martin14
 
< 0.1%
Eddie Palmer13
 
< 0.1%
Yshwa13
 
< 0.1%
J Hacha de Zola11
 
< 0.1%
Brian Kelly9
 
< 0.1%
Allister Thompson9
 
< 0.1%
Other values (56)137
 
0.1%
(Missing)106263
99.7%
2021-02-20T10:12:46.380056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tomcat44
 
6.6%
apache44
 
6.6%
myers24
 
3.6%
wayne24
 
3.6%
wesley19
 
2.9%
entity19
 
2.9%
wizard19
 
2.9%
shadow19
 
2.9%
willis18
 
2.7%
martin17
 
2.6%
Other values (122)419
62.9%

Most occurring characters

ValueCountFrequency (%)
a388
 
9.7%
e362
 
9.0%
358
 
8.9%
i245
 
6.1%
l204
 
5.1%
r201
 
5.0%
n193
 
4.8%
t181
 
4.5%
o166
 
4.1%
h162
 
4.0%
Other values (48)1550
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2972
74.1%
Uppercase Letter669
 
16.7%
Space Separator358
 
8.9%
Other Punctuation7
 
0.2%
Dash Punctuation2
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
W90
13.5%
A86
12.9%
T61
 
9.1%
M56
 
8.4%
E49
 
7.3%
K40
 
6.0%
J33
 
4.9%
S33
 
4.9%
L24
 
3.6%
C24
 
3.6%
Other values (16)173
25.9%
ValueCountFrequency (%)
a388
13.1%
e362
12.2%
i245
 
8.2%
l204
 
6.9%
r201
 
6.8%
n193
 
6.5%
t181
 
6.1%
o166
 
5.6%
h162
 
5.5%
s160
 
5.4%
Other values (15)710
23.9%
ValueCountFrequency (%)
/3
42.9%
.2
28.6%
,2
28.6%
ValueCountFrequency (%)
358
100.0%
ValueCountFrequency (%)
-2
100.0%
ValueCountFrequency (%)
(1
100.0%
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3641
90.8%
Common369
 
9.2%

Most frequent character per script

ValueCountFrequency (%)
a388
 
10.7%
e362
 
9.9%
i245
 
6.7%
l204
 
5.6%
r201
 
5.5%
n193
 
5.3%
t181
 
5.0%
o166
 
4.6%
h162
 
4.4%
s160
 
4.4%
Other values (41)1379
37.9%
ValueCountFrequency (%)
358
97.0%
/3
 
0.8%
-2
 
0.5%
.2
 
0.5%
,2
 
0.5%
(1
 
0.3%
)1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4010
100.0%

Most frequent character per block

ValueCountFrequency (%)
a388
 
9.7%
e362
 
9.0%
358
 
8.9%
i245
 
6.1%
l204
 
5.1%
r201
 
5.0%
n193
 
4.8%
t181
 
4.5%
o166
 
4.1%
h162
 
4.0%
Other values (48)1550
38.7%

number
Real number (ℝ≥0)

ZEROS

Distinct246
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.260945446
Minimum0
Maximum255
Zeros7894
Zeros (%)7.4%
Memory size832.7 KiB
2021-02-20T10:12:46.445376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile25
Maximum255
Range255
Interquartile range (IQR)7

Descriptive statistics

Standard deviation15.24327144
Coefficient of variation (CV)1.845221172
Kurtosis73.08313917
Mean8.260945446
Median Absolute Deviation (MAD)3
Skewness7.046228851
Sum880402
Variance232.3573242
MonotocityNot monotonic
2021-02-20T10:12:46.521144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112419
11.7%
211214
10.5%
310379
9.7%
49551
9.0%
58471
 
7.9%
07894
 
7.4%
67269
 
6.8%
76178
 
5.8%
85276
 
5.0%
94442
 
4.2%
Other values (236)23481
22.0%
ValueCountFrequency (%)
07894
7.4%
112419
11.7%
211214
10.5%
310379
9.7%
49551
9.0%
ValueCountFrequency (%)
25533
< 0.1%
2491
 
< 0.1%
2481
 
< 0.1%
2471
 
< 0.1%
2461
 
< 0.1%

publisher
Categorical

HIGH CARDINALITY
MISSING

Distinct135
Distinct (%)10.7%
Missing105311
Missing (%)98.8%
Memory size832.7 KiB
Victrola Dog (ASCAP)
465 
You've Been a Wonderful Laugh Track (ASCAP)
84 
Section 27
73 
www.headphonica.com
55 
Studio 11
 
43
Other values (130)
543 

Length

Max length51
Median length20
Mean length18.89548694
Min length3

Characters and Unicode

Total characters23865
Distinct characters74
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)6.4%

Sample

1st rowCherry Red Music (UK)
2nd rowCherry Red Music (UK)
3rd rowCherry Red Music (UK)
4th rowCherry Red Music (UK)
5th rowCherry Red Music (UK)
ValueCountFrequency (%)
Victrola Dog (ASCAP)465
 
0.4%
You've Been a Wonderful Laugh Track (ASCAP)84
 
0.1%
Section 2773
 
0.1%
www.headphonica.com55
 
0.1%
Studio 1143
 
< 0.1%
Toucan Music32
 
< 0.1%
Mitoma Industries32
 
< 0.1%
Radius23
 
< 0.1%
Cherry Red Music (UK)20
 
< 0.1%
Andy G. Cohen19
 
< 0.1%
Other values (125)417
 
0.4%
(Missing)105311
98.8%
2021-02-20T10:12:46.674198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ascap566
 
16.0%
dog465
 
13.2%
victrola465
 
13.2%
a86
 
2.4%
been84
 
2.4%
laugh84
 
2.4%
wonderful84
 
2.4%
you've84
 
2.4%
track84
 
2.4%
music76
 
2.2%
Other values (204)1453
41.1%

Most occurring characters

ValueCountFrequency (%)
2268
 
9.5%
o1897
 
7.9%
a1291
 
5.4%
A1246
 
5.2%
i1168
 
4.9%
e1097
 
4.6%
c1058
 
4.4%
r976
 
4.1%
t928
 
3.9%
l914
 
3.8%
Other values (64)11022
46.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13876
58.1%
Uppercase Letter5682
23.8%
Space Separator2268
 
9.5%
Open Punctuation596
 
2.5%
Close Punctuation596
 
2.5%
Decimal Number454
 
1.9%
Other Punctuation375
 
1.6%
Dash Punctuation18
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
o1897
13.7%
a1291
 
9.3%
i1168
 
8.4%
e1097
 
7.9%
c1058
 
7.6%
r976
 
7.0%
t928
 
6.7%
l914
 
6.6%
u668
 
4.8%
g642
 
4.6%
Other values (17)3237
23.3%
ValueCountFrequency (%)
A1246
21.9%
S792
13.9%
C706
12.4%
P612
10.8%
D497
 
8.7%
V490
 
8.6%
M163
 
2.9%
T162
 
2.9%
L140
 
2.5%
R138
 
2.4%
Other values (15)736
13.0%
ValueCountFrequency (%)
1123
27.1%
2102
22.5%
790
19.8%
838
 
8.4%
322
 
4.8%
520
 
4.4%
420
 
4.4%
016
 
3.5%
612
 
2.6%
911
 
2.4%
ValueCountFrequency (%)
.188
50.1%
'84
22.4%
/48
 
12.8%
,20
 
5.3%
:19
 
5.1%
&7
 
1.9%
;7
 
1.9%
!2
 
0.5%
ValueCountFrequency (%)
2268
100.0%
ValueCountFrequency (%)
(596
100.0%
ValueCountFrequency (%)
)596
100.0%
ValueCountFrequency (%)
-18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19558
82.0%
Common4307
 
18.0%

Most frequent character per script

ValueCountFrequency (%)
o1897
 
9.7%
a1291
 
6.6%
A1246
 
6.4%
i1168
 
6.0%
e1097
 
5.6%
c1058
 
5.4%
r976
 
5.0%
t928
 
4.7%
l914
 
4.7%
S792
 
4.0%
Other values (42)8191
41.9%
ValueCountFrequency (%)
2268
52.7%
(596
 
13.8%
)596
 
13.8%
.188
 
4.4%
1123
 
2.9%
2102
 
2.4%
790
 
2.1%
'84
 
2.0%
/48
 
1.1%
838
 
0.9%
Other values (12)174
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII23862
> 99.9%
None3
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
2268
 
9.5%
o1897
 
7.9%
a1291
 
5.4%
A1246
 
5.2%
i1168
 
4.9%
e1097
 
4.6%
c1058
 
4.4%
r976
 
4.1%
t928
 
3.9%
l914
 
3.8%
Other values (62)11019
46.2%
ValueCountFrequency (%)
ü2
66.7%
é1
33.3%

tags
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size832.7 KiB

title
Categorical

HIGH CARDINALITY

Distinct94986
Distinct (%)89.1%
Missing1
Missing (%)< 0.1%
Memory size832.7 KiB
Untitled
 
298
Into Infinity "ear" loop
 
150
Intro
 
115
Interview
 
92
Chicken and Cheese 2 (Foot Village cover)
 
49
Other values (94981)
105869 

Length

Max length177
Median length14
Mean length17.09052011
Min length1

Characters and Unicode

Total characters1821388
Distinct characters664
Distinct categories21 ?
Distinct scripts10 ?
Distinct blocks21 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88713 ?
Unique (%)83.2%

Sample

1st rowFood
2nd rowElectric Ave
3rd rowThis World
4th rowFreeway
5th rowSpiritual Level
ValueCountFrequency (%)
Untitled298
 
0.3%
Into Infinity "ear" loop150
 
0.1%
Intro115
 
0.1%
Interview92
 
0.1%
Chicken and Cheese 2 (Foot Village cover)49
 
< 0.1%
One Minute For The Stars36
 
< 0.1%
jingles, interludes et dimanche minuit34
 
< 0.1%
untitled30
 
< 0.1%
Title Unknown29
 
< 0.1%
[interview]29
 
< 0.1%
Other values (94976)105711
99.2%
2021-02-20T10:12:46.981926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the11757
 
3.7%
of4699
 
1.5%
4608
 
1.4%
a3994
 
1.3%
in3338
 
1.0%
to2399
 
0.8%
i2392
 
0.8%
and2337
 
0.7%
you2165
 
0.7%
on1669
 
0.5%
Other values (61318)278738
87.6%

Most occurring characters

ValueCountFrequency (%)
211992
 
11.6%
e162056
 
8.9%
a110428
 
6.1%
o104061
 
5.7%
i98392
 
5.4%
n96053
 
5.3%
t89571
 
4.9%
r89064
 
4.9%
s70139
 
3.9%
l63421
 
3.5%
Other values (654)726211
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1251514
68.7%
Uppercase Letter271681
 
14.9%
Space Separator212009
 
11.6%
Decimal Number31681
 
1.7%
Other Punctuation23638
 
1.3%
Close Punctuation11023
 
0.6%
Open Punctuation11016
 
0.6%
Dash Punctuation6178
 
0.3%
Connector Punctuation1266
 
0.1%
Other Letter454
 
< 0.1%
Other values (11)928
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
º28
 
6.2%
11
 
2.4%
10
 
2.2%
10
 
2.2%
7
 
1.5%
7
 
1.5%
ا7
 
1.5%
6
 
1.3%
6
 
1.3%
5
 
1.1%
Other values (244)357
78.6%
ValueCountFrequency (%)
e162056
12.9%
a110428
 
8.8%
o104061
 
8.3%
i98392
 
7.9%
n96053
 
7.7%
t89571
 
7.2%
r89064
 
7.1%
s70139
 
5.6%
l63421
 
5.1%
h45222
 
3.6%
Other values (153)323107
25.8%
ValueCountFrequency (%)
S24584
 
9.0%
T24009
 
8.8%
M16800
 
6.2%
A16723
 
6.2%
I15954
 
5.9%
B15191
 
5.6%
C15005
 
5.5%
D14143
 
5.2%
L12919
 
4.8%
P12129
 
4.5%
Other values (115)104224
38.4%
ValueCountFrequency (%)
.6723
28.4%
'5700
24.1%
,3608
15.3%
/1852
 
7.8%
:1231
 
5.2%
"954
 
4.0%
&921
 
3.9%
!877
 
3.7%
#614
 
2.6%
?572
 
2.4%
Other values (16)586
 
2.5%
ValueCountFrequency (%)
°27
29.3%
15
16.3%
13
14.1%
5
 
5.4%
4
 
4.3%
4
 
4.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
2
 
2.2%
Other values (11)13
14.1%
ValueCountFrequency (%)
+256
64.6%
=38
 
9.6%
|31
 
7.8%
~26
 
6.6%
>18
 
4.5%
<8
 
2.0%
7
 
1.8%
¬2
 
0.5%
2
 
0.5%
2
 
0.5%
Other values (5)6
 
1.5%
ValueCountFrequency (%)
́107
56.6%
̈23
 
12.2%
̀16
 
8.5%
̶12
 
6.3%
̂8
 
4.2%
̃7
 
3.7%
̧6
 
3.2%
̌3
 
1.6%
̊3
 
1.6%
̄2
 
1.1%
Other values (2)2
 
1.1%
ValueCountFrequency (%)
16652
21.0%
06615
20.9%
25182
16.4%
32918
9.2%
42180
 
6.9%
51901
 
6.0%
61765
 
5.6%
91600
 
5.1%
71496
 
4.7%
81372
 
4.3%
ValueCountFrequency (%)
’4
30.8%
ƒ2
15.4%
2
15.4%
…2
15.4%
1
 
7.7%
1
 
7.7%
š1
 
7.7%
ValueCountFrequency (%)
(9986
90.6%
[959
 
8.7%
{70
 
0.6%
1
 
< 0.1%
ValueCountFrequency (%)
)9984
90.6%
]964
 
8.7%
}74
 
0.7%
1
 
< 0.1%
ValueCountFrequency (%)
´42
53.2%
^19
24.1%
`12
 
15.2%
¨6
 
7.6%
ValueCountFrequency (%)
211992
> 99.9%
 16
 
< 0.1%
 1
 
< 0.1%
ValueCountFrequency (%)
-6160
99.7%
15
 
0.2%
3
 
< 0.1%
ValueCountFrequency (%)
$23
65.7%
¤9
 
25.7%
£3
 
8.6%
ValueCountFrequency (%)
54
84.4%
10
 
15.6%
ValueCountFrequency (%)
11
64.7%
6
35.3%
ValueCountFrequency (%)
29
87.9%
­4
 
12.1%
ValueCountFrequency (%)
²2
66.7%
³1
33.3%
ValueCountFrequency (%)
_1266
100.0%
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1511511
83.0%
Common297552
 
16.3%
Cyrillic11266
 
0.6%
Greek444
 
< 0.1%
Inherited189
 
< 0.1%
Han180
 
< 0.1%
Hiragana139
 
< 0.1%
Katakana69
 
< 0.1%
Arabic26
 
< 0.1%
Hebrew12
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e162056
 
10.7%
a110428
 
7.3%
o104061
 
6.9%
i98392
 
6.5%
n96053
 
6.4%
t89571
 
5.9%
r89064
 
5.9%
s70139
 
4.6%
l63421
 
4.2%
h45222
 
3.0%
Other values (153)583104
38.6%
ValueCountFrequency (%)
4
 
2.2%
3
 
1.7%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
Other values (138)157
87.2%
ValueCountFrequency (%)
211992
71.2%
(9986
 
3.4%
)9984
 
3.4%
.6723
 
2.3%
16652
 
2.2%
06615
 
2.2%
-6160
 
2.1%
'5700
 
1.9%
25182
 
1.7%
,3608
 
1.2%
Other values (101)24950
 
8.4%
ValueCountFrequency (%)
о997
 
8.8%
а940
 
8.3%
и871
 
7.7%
е789
 
7.0%
н645
 
5.7%
т582
 
5.2%
р550
 
4.9%
с463
 
4.1%
л411
 
3.6%
в401
 
3.6%
Other values (58)4617
41.0%
ValueCountFrequency (%)
α49
 
11.0%
ο39
 
8.8%
ε32
 
7.2%
ι30
 
6.8%
ν28
 
6.3%
τ26
 
5.9%
υ23
 
5.2%
σ21
 
4.7%
ς17
 
3.8%
ρ17
 
3.8%
Other values (47)162
36.5%
ValueCountFrequency (%)
11
 
7.9%
10
 
7.2%
10
 
7.2%
7
 
5.0%
7
 
5.0%
6
 
4.3%
6
 
4.3%
5
 
3.6%
5
 
3.6%
4
 
2.9%
Other values (33)68
48.9%
ValueCountFrequency (%)
4
 
5.8%
4
 
5.8%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
Other values (27)37
53.6%
ValueCountFrequency (%)
ا7
26.9%
ل5
19.2%
و2
 
7.7%
ق1
 
3.8%
ذ1
 
3.8%
ف1
 
3.8%
ي1
 
3.8%
م1
 
3.8%
ز1
 
3.8%
ن1
 
3.8%
Other values (5)5
19.2%
ValueCountFrequency (%)
́107
56.6%
̈23
 
12.2%
̀16
 
8.5%
̶12
 
6.3%
̂8
 
4.2%
̃7
 
3.7%
̧6
 
3.2%
̌3
 
1.6%
̊3
 
1.6%
̄2
 
1.1%
Other values (2)2
 
1.1%
ValueCountFrequency (%)
ש2
16.7%
ב2
16.7%
ז1
8.3%
מ1
8.3%
ן1
8.3%
ע1
8.3%
ו1
8.3%
ר1
8.3%
ל1
8.3%
א1
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1805428
99.1%
Cyrillic11266
 
0.6%
None3826
 
0.2%
Diacriticals188
 
< 0.1%
CJK180
 
< 0.1%
Punctuation155
 
< 0.1%
Hiragana139
 
< 0.1%
Katakana76
 
< 0.1%
Geometric Shapes31
 
< 0.1%
Arabic27
 
< 0.1%
Other values (11)72
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
211992
 
11.7%
e162056
 
9.0%
a110428
 
6.1%
o104061
 
5.8%
i98392
 
5.4%
n96053
 
5.3%
t89571
 
5.0%
r89064
 
4.9%
s70139
 
3.9%
l63421
 
3.5%
Other values (86)710251
39.3%
ValueCountFrequency (%)
é920
24.0%
ä201
 
5.3%
ó178
 
4.7%
á167
 
4.4%
è157
 
4.1%
ö136
 
3.6%
í126
 
3.3%
à108
 
2.8%
ü105
 
2.7%
ê85
 
2.2%
Other values (175)1643
42.9%
ValueCountFrequency (%)
́107
56.9%
̈23
 
12.2%
̀16
 
8.5%
̶12
 
6.4%
̂8
 
4.3%
̃7
 
3.7%
̧6
 
3.2%
̌3
 
1.6%
̊3
 
1.6%
̄2
 
1.1%
ValueCountFrequency (%)
54
34.8%
29
18.7%
15
 
9.7%
11
 
7.1%
10
 
6.5%
9
 
5.8%
6
 
3.9%
5
 
3.2%
4
 
2.6%
4
 
2.6%
Other values (3)8
 
5.2%
ValueCountFrequency (%)
о997
 
8.8%
а940
 
8.3%
и871
 
7.7%
е789
 
7.0%
н645
 
5.7%
т582
 
5.2%
р550
 
4.9%
с463
 
4.1%
л411
 
3.6%
в401
 
3.6%
Other values (58)4617
41.0%
ValueCountFrequency (%)
5
55.6%
4
44.4%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
7
 
9.2%
4
 
5.3%
4
 
5.3%
4
 
5.3%
4
 
5.3%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
2
 
2.6%
Other values (28)39
51.3%
ValueCountFrequency (%)
11
 
7.9%
10
 
7.2%
10
 
7.2%
7
 
5.0%
7
 
5.0%
6
 
4.3%
6
 
4.3%
5
 
3.6%
5
 
3.6%
4
 
2.9%
Other values (33)68
48.9%
ValueCountFrequency (%)
4
 
2.2%
3
 
1.7%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
2
 
1.1%
Other values (138)157
87.2%
ValueCountFrequency (%)
4
22.2%
3
16.7%
3
16.7%
2
11.1%
2
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
ValueCountFrequency (%)
15
48.4%
13
41.9%
1
 
3.2%
1
 
3.2%
1
 
3.2%
ValueCountFrequency (%)
ש2
16.7%
ב2
16.7%
ז1
8.3%
מ1
8.3%
ן1
8.3%
ע1
8.3%
ו1
8.3%
ר1
8.3%
ל1
8.3%
א1
8.3%
ValueCountFrequency (%)
ا7
25.9%
ل5
18.5%
و2
 
7.4%
ق1
 
3.7%
ذ1
 
3.7%
ف1
 
3.7%
ي1
 
3.7%
ٔ1
 
3.7%
م1
 
3.7%
ز1
 
3.7%
Other values (6)6
22.2%
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
ɱ2
40.0%
ə1
20.0%
ʛ1
20.0%
ɫ1
20.0%
ValueCountFrequency (%)
7
63.6%
2
 
18.2%
1
 
9.1%
1
 
9.1%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
ValueCountFrequency (%)
2
100.0%

Interactions

2021-02-20T10:12:38.550123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:38.632370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:38.714720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:38.796632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:38.879945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:38.961276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.041984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.120111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.195211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.267412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.340083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.412322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.483871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.554356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.623047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.704449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.778727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.857002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:39.935115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.012723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.088583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.164108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.246551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.321349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.399919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.478695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.556758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.633799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.709578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.790960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.865328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:40.943390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.022392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.100840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.177506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.252633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-20T10:12:41.407419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.484994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.563454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.641717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.717875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.792375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.870423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:41.941268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.015483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.091074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.166004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.240151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.311472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.388904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.458980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.532198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.606444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.680812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:12:42.754183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-20T10:12:47.061513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-20T10:12:47.146444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-20T10:12:47.231265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-20T10:12:47.321179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-20T10:12:47.409802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-20T10:12:43.157564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-20T10:12:43.501505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-20T10:12:43.843077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-20T10:12:43.993073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

track_idbit_ratecommentscomposerdate_createddate_recordeddurationfavoritesgenre_topgenresgenres_allinformationinterestlanguage_codelicenselistenslyricistnumberpublishertagstitle
022560000NaN2008-11-26 01:48:122008-11-261682Hip-Hop[21][21]NaN4656enAttribution-NonCommercial-ShareAlike 3.0 International1293NaN3NaN[]Food
132560000NaN2008-11-26 01:48:142008-11-262371Hip-Hop[21][21]NaN1470enAttribution-NonCommercial-ShareAlike 3.0 International514NaN4NaN[]Electric Ave
252560000NaN2008-11-26 01:48:202008-11-262066Hip-Hop[21][21]NaN1933enAttribution-NonCommercial-ShareAlike 3.0 International1151NaN6NaN[]This World
3101920000Kurt Vile2008-11-25 17:49:062008-11-26161178Pop[10][10]NaN54881enAttribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International50135NaN1NaN[]Freeway
4202560000NaN2008-11-26 01:48:562008-01-013110NaN[76, 103][17, 10, 76, 103]NaN978enAttribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International361NaN3NaN[]Spiritual Level
5262560000NaN2008-11-26 01:49:052008-01-011810NaN[76, 103][17, 10, 76, 103]NaN1060enAttribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International193NaN4NaN[]Where is your Love?
6302560000NaN2008-11-26 01:49:112008-01-011740NaN[76, 103][17, 10, 76, 103]NaN718enAttribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International612NaN5NaN[]Too Happy
7462560000NaN2008-11-26 01:49:532008-01-011040NaN[76, 103][17, 10, 76, 103]NaN252enAttribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International171NaN8NaN[]Yosemite
8482560000NaN2008-11-26 01:49:562008-01-012050NaN[76, 103][17, 10, 76, 103]NaN247enAttribution-NonCommercial-NoDerivatives (aka Music Sharing) 3.0 International173NaN9NaN[]Light of Light
91342560000NaN2008-11-26 01:43:192008-11-262073Hip-Hop[21][21]NaN1126enAttribution-NonCommercial-ShareAlike 3.0 International943NaN5NaN[]Street Music

Last rows

track_idbit_ratecommentscomposerdate_createddate_recordeddurationfavoritesgenre_topgenresgenres_allinformationinterestlanguage_codelicenselistenslyricistnumberpublishertagstitle
1065641553103200000NaN2017-03-30 12:53:22NaT7260Experimental[1][1, 38]NaN94NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.084NaN4NaN[]Raman Abhishek
1065651553113200000NaN2017-03-30 12:53:24NaT5390Experimental[1][1, 38]NaN187NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.0171NaN5NaN[]Na Potehu Telu
1065661553123200000NaN2017-03-30 12:53:25NaT5270Experimental[1][1, 38]NaN230NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.0219NaN6NaN[]Koza-Korova
1065671553143200000NaN2017-03-30 15:23:31NaT1770Rock[25][25, 12]NaN778NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.0652NaN2NaN[]Miracle Grow
1065681553153200000NaN2017-03-30 15:23:33NaT381Rock[25][25, 12]NaN153NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.0128NaN1NaN[]Space Power Over-Watch Destroying Evil Rats
1065691553163200000NaN2017-03-30 15:23:34NaT1621Rock[25][25, 12]NaN122NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.0102NaN3NaN[]The Auger
1065701553173200000NaN2017-03-30 15:23:36NaT2171Rock[25][25, 12]NaN194NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.0165NaN4NaN[]Let's Skin Ruby
1065711553183200000NaN2017-03-30 15:23:37NaT4042Rock[25][25, 12]NaN214NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.0168NaN6NaN[]My House Smells Like Kim Deal/Pulp
1065721553193200000NaN2017-03-30 15:23:39NaT1460Rock[25][25, 12]NaN336NaNCreative Commons Attribution-NonCommercial-NoDerivatives 4.0294NaN5NaN[]The Man With Two Mouths
1065731553203200000NaN2017-03-30 09:15:36NaT1981NaN[10, 12, 169][169, 10, 12, 9]NaN972NaNAttribution-NonCommercial705NaN7NaN[ballad, epic, rockabilly, curse, hex, hard rock, cauldron, witches, creepy, black cats]Another Trick Up My Sleeve (Instrumental)